Berkeley Earth

1.1 Charts - Timeseries

Exercise: Visualize the evolution of the temperature anomaly monthly average over time with a timeseries chart

Data: 'data/Land_Ocean_Monthly_Anomaly_Average.csv'

Tips:

import pandas as pd
pd.read_csv()
pd.to_datetime()



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import pandas as pd
from bokeh.charts import TimeSeries, output_notebook, show



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# Get data
df = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv')



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# Process data
df['datetime'] = pd.to_datetime(df['datetime'])
df = df[['anomaly','datetime']]



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# Output option
output_notebook()



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# Create timeseries chart
t = TimeSeries(df, x='datetime')



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# Show chart
show(t)

Exercise: Style your plot

Ideas:

Add a title
Add axis labels
Change width and height
Deactivate toolbox or customize available tools
Change line color

Charts arguments can be found: http://bokeh.pydata.org/en/latest/docs/user_guide/charts.html#generic-arguments



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# Style your timeseries chart 
t = TimeSeries(df, x='datetime', xlabel='time', ylabel='Anomaly(ºC)',
               xgrid = False, ygrid=True, tools=False, width=950, height=300,
               title="Temperature Anomaly(ºC) Monthly Average", palette=["grey"])



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# Show new chart
show(t)

Exercise: Add the moving annual average to your chart

Tips:

pd.rolling_mean()



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# Compute moving average
df['moving_average'] = pd.rolling_mean(df['anomaly'], 12)



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# Create chart with moving average
t = TimeSeries(df, x='datetime', xlabel='time', ylabel='Anomaly(ºC)',
               xgrid = False, ygrid=True, tools=False, width=950, height=300, legend="bottom_right",
               title="Temperature Anomaly(ºC) Monthly Average", palette=["grey", "red"])



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# Show chart with moving average
show(t)



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